Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations4063
Missing cells54831
Missing cells (%)38.6%
Duplicate rows330
Duplicate rows (%)8.1%
Total size in memory1.2 MiB
Average record size in memory320.5 B

Variable types

Categorical29
Numeric5
Unsupported1

Alerts

solar_energy_used_for_agricultural_systems has constant value "5.0" Constant
solar_energy_used_for_all_above has constant value "6.0" Constant
biogas_used_for_cooking has constant value "2" Constant
Dataset has 330 (8.1%) duplicate rowsDuplicates
aware_of_no_of_units_generated_by_solar_system is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 3 other fieldsHigh correlation
boil_water_before_drinking is highly overall correlated with source_of_energy_for_boiling_drinking_waterHigh correlation
coconut_shells_or_charcoal_used_for_cooking is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 11 other fieldsHigh correlation
does_water_heating_equipment_serve_other_housing_units is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 4 other fieldsHigh correlation
firewood_used_for_cooking is highly overall correlated with solar_energy_used_for_car_charging and 1 other fieldsHigh correlation
gas_used_for_cooking is highly overall correlated with solar_energy_used_for_car_charging and 3 other fieldsHigh correlation
generate_electicity_using_bio_energy is highly overall correlated with solar_energy_used_for_car_charging and 3 other fieldsHigh correlation
generate_electicity_using_mini_hydropower is highly overall correlated with no_of_units_generated_by_solar_system and 4 other fieldsHigh correlation
generate_electicity_using_other_methods is highly overall correlated with solar_energy_used_for_car_charging and 3 other fieldsHigh correlation
generate_electicity_using_solar_energy is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 9 other fieldsHigh correlation
generate_electicity_using_wind_power is highly overall correlated with no_of_units_generated_by_solar_system and 4 other fieldsHigh correlation
have_backup_generator is highly overall correlated with solar_energy_used_for_car_chargingHigh correlation
household_members_used_hot_water_last_week is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
kerosene_used_for_cooking is highly overall correlated with solar_energy_used_for_car_charging and 3 other fieldsHigh correlation
method_of_receiving_water is highly overall correlated with solar_energy_used_for_car_charging and 2 other fieldsHigh correlation
no_of_times_food_cooked_last_week is highly overall correlated with solar_energy_used_for_car_chargingHigh correlation
no_of_units_generated_by_solar_system is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 7 other fieldsHigh correlation
other_methods_used_for_cooking is highly overall correlated with solar_energy_used_for_car_charging and 2 other fieldsHigh correlation
sawdust_or_paddy_husk_used_for_cooking is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 10 other fieldsHigh correlation
solar_energy_used_for_car_charging is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 23 other fieldsHigh correlation
solar_energy_used_for_cooking is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 17 other fieldsHigh correlation
solar_energy_used_for_outdoor_lighting is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 17 other fieldsHigh correlation
solar_energy_used_for_water_heating is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 11 other fieldsHigh correlation
solar_system_invertor_or_noninvertor is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 3 other fieldsHigh correlation
solar_system_ongrid_or_offgird is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 6 other fieldsHigh correlation
source_of_energy_for_boiling_drinking_water is highly overall correlated with boil_water_before_drinking and 5 other fieldsHigh correlation
water_heating_method_for_bathing is highly overall correlated with generate_electicity_using_solar_energy and 3 other fieldsHigh correlation
when_was_solar_system_installed is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
have_backup_generator is highly imbalanced (81.7%) Imbalance
generate_electicity_using_solar_energy is highly imbalanced (53.2%) Imbalance
generate_electicity_using_bio_energy is highly imbalanced (95.1%) Imbalance
generate_electicity_using_mini_hydropower is highly imbalanced (97.9%) Imbalance
generate_electicity_using_wind_power is highly imbalanced (97.5%) Imbalance
generate_electicity_using_other_methods is highly imbalanced (96.5%) Imbalance
have_system_to_store_backup_energy is highly imbalanced (61.6%) Imbalance
does_water_heating_equipment_serve_other_housing_units is highly imbalanced (57.0%) Imbalance
electricity_generated_using_solar_energy_used_for_cooking is highly imbalanced (90.9%) Imbalance
kerosene_used_for_cooking is highly imbalanced (89.1%) Imbalance
sawdust_or_paddy_husk_used_for_cooking is highly imbalanced (97.9%) Imbalance
coconut_shells_or_charcoal_used_for_cooking is highly imbalanced (99.7%) Imbalance
other_methods_used_for_cooking is highly imbalanced (90.0%) Imbalance
solar_system_ongrid_or_offgird has 3658 (90.0%) missing values Missing
solar_system_invertor_or_noninvertor has 3658 (90.0%) missing values Missing
solar_energy_used_for_water_heating has 3988 (98.2%) missing values Missing
solar_energy_used_for_cooking has 4050 (99.7%) missing values Missing
solar_energy_used_for_outdoor_lighting has 4054 (99.8%) missing values Missing
solar_energy_used_for_car_charging has 4060 (99.9%) missing values Missing
solar_energy_used_for_agricultural_systems has 4062 (> 99.9%) missing values Missing
solar_energy_used_for_all_above has 4062 (> 99.9%) missing values Missing
solar_energy_used_for_other_purposes has 4063 (100.0%) missing values Missing
aware_of_no_of_units_generated_by_solar_system has 3658 (90.0%) missing values Missing
no_of_units_generated_by_solar_system has 3868 (95.2%) missing values Missing
when_was_solar_system_installed has 3658 (90.0%) missing values Missing
does_water_heating_equipment_serve_other_housing_units has 3302 (81.3%) missing values Missing
household_members_used_hot_water_last_week has 2457 (60.5%) missing values Missing
source_of_energy_for_boiling_drinking_water has 2233 (55.0%) missing values Missing
solar_energy_used_for_other_purposes is an unsupported type, check if it needs cleaning or further analysis Unsupported
water_heating_method_for_bathing has 507 (12.5%) zeros Zeros

Reproduction

Analysis started2024-11-18 09:00:14.109290
Analysis finished2024-11-18 09:00:19.338282
Duration5.23 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

have_backup_generator
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
3950 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

Length

2024-11-18T14:30:19.388463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:19.470715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

Most occurring characters

ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3950
97.2%
1 113
 
2.8%

generate_electicity_using_solar_energy
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
2
3658 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

Length

2024-11-18T14:30:19.551920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:19.632104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

Most occurring characters

ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3658
90.0%
1 405
 
10.0%

generate_electicity_using_bio_energy
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4041 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

Length

2024-11-18T14:30:19.714991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:19.794360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4041
99.5%
1 22
 
0.5%

generate_electicity_using_mini_hydropower
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4055 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Length

2024-11-18T14:30:19.875749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:19.955539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

generate_electicity_using_wind_power
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4053 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

Length

2024-11-18T14:30:20.042798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:20.122022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4053
99.8%
1 10
 
0.2%

generate_electicity_using_other_methods
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4048 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

Length

2024-11-18T14:30:20.203758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:20.284422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4048
99.6%
1 15
 
0.4%

solar_system_ongrid_or_offgird
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size192.5 KiB
1.0
330 
2.0
75 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1215
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 330
 
8.1%
2.0 75
 
1.8%
(Missing) 3658
90.0%

Length

2024-11-18T14:30:20.367847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:20.447961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 330
81.5%
2.0 75
 
18.5%

Most occurring characters

ValueCountFrequency (%)
. 405
33.3%
0 405
33.3%
1 330
27.2%
2 75
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 405
33.3%
0 405
33.3%
1 330
27.2%
2 75
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 405
33.3%
0 405
33.3%
1 330
27.2%
2 75
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 405
33.3%
0 405
33.3%
1 330
27.2%
2 75
 
6.2%

solar_system_invertor_or_noninvertor
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size192.5 KiB
1.0
308 
0.0
97 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 308
 
7.6%
0.0 97
 
2.4%
(Missing) 3658
90.0%

Length

2024-11-18T14:30:20.532981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:20.612159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 308
76.0%
0.0 97
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 502
41.3%
. 405
33.3%
1 308
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 502
41.3%
. 405
33.3%
1 308
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 502
41.3%
. 405
33.3%
1 308
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 502
41.3%
. 405
33.3%
1 308
25.3%

solar_energy_used_for_water_heating
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)8.0%
Missing3988
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean2.9066667
Minimum0
Maximum7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:30:20.686468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q33
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2005732
Coefficient of variation (CV)0.75707794
Kurtosis-0.59971914
Mean2.9066667
Median Absolute Deviation (MAD)2
Skewness0.85650049
Sum218
Variance4.8425225
MonotonicityNot monotonic
2024-11-18T14:30:20.770667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 32
 
0.8%
3 25
 
0.6%
7 11
 
0.3%
6 5
 
0.1%
0 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 3988
98.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 32
0.8%
3 25
0.6%
4 1
 
< 0.1%
6 5
 
0.1%
7 11
 
0.3%
ValueCountFrequency (%)
7 11
 
0.3%
6 5
 
0.1%
4 1
 
< 0.1%
3 25
0.6%
1 32
0.8%
0 1
 
< 0.1%

solar_energy_used_for_cooking
Categorical

High correlation  Missing 

Distinct2
Distinct (%)15.4%
Missing4050
Missing (%)99.7%
Memory size192.5 KiB
0.0
10 
7.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row7.0
3rd row7.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10
 
0.2%
7.0 3
 
0.1%
(Missing) 4050
99.7%

Length

2024-11-18T14:30:20.868731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:20.947027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10
76.9%
7.0 3
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
7 3
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
7 3
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
7 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23
59.0%
. 13
33.3%
7 3
 
7.7%

solar_energy_used_for_outdoor_lighting
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing4054
Missing (%)99.8%
Memory size192.5 KiB
3.0
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 8
 
0.2%
6.0 1
 
< 0.1%
(Missing) 4054
99.8%

Length

2024-11-18T14:30:21.026984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:21.108077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 8
88.9%
6.0 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
3 8
29.6%
6 1
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
3 8
29.6%
6 1
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
3 8
29.6%
6 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
3 8
29.6%
6 1
 
3.7%

solar_energy_used_for_car_charging
Categorical

High correlation  Missing 

Distinct2
Distinct (%)66.7%
Missing4060
Missing (%)99.9%
Memory size192.5 KiB
4.0
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row4.0
2nd row4.0
3rd row6.0

Common Values

ValueCountFrequency (%)
4.0 2
 
< 0.1%
6.0 1
 
< 0.1%
(Missing) 4060
99.9%

Length

2024-11-18T14:30:21.194392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:21.272909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 2
66.7%
6.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
4 2
22.2%
6 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
4 2
22.2%
6 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
4 2
22.2%
6 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
4 2
22.2%
6 1
 
11.1%

solar_energy_used_for_agricultural_systems
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4062
Missing (%)> 99.9%
Memory size192.5 KiB
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row5.0

Common Values

ValueCountFrequency (%)
5.0 1
 
< 0.1%
(Missing) 4062
> 99.9%

Length

2024-11-18T14:30:21.354231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:21.425737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1
33.3%
. 1
33.3%
0 1
33.3%

solar_energy_used_for_all_above
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing4062
Missing (%)> 99.9%
Memory size192.5 KiB
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row6.0

Common Values

ValueCountFrequency (%)
6.0 1
 
< 0.1%
(Missing) 4062
> 99.9%

Length

2024-11-18T14:30:21.501375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:21.571852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

solar_energy_used_for_other_purposes
Unsupported

Missing  Rejected  Unsupported 

Missing4063
Missing (%)100.0%
Memory size192.5 KiB

aware_of_no_of_units_generated_by_solar_system
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size192.5 KiB
0.0
210 
1.0
195 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 210
 
5.2%
1.0 195
 
4.8%
(Missing) 3658
90.0%

Length

2024-11-18T14:30:21.647244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:21.725926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 210
51.9%
1.0 195
48.1%

Most occurring characters

ValueCountFrequency (%)
0 615
50.6%
. 405
33.3%
1 195
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 615
50.6%
. 405
33.3%
1 195
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 615
50.6%
. 405
33.3%
1 195
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 615
50.6%
. 405
33.3%
1 195
 
16.0%

no_of_units_generated_by_solar_system
Real number (ℝ)

High correlation  Missing 

Distinct102
Distinct (%)52.3%
Missing3868
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean445.34718
Minimum0
Maximum2500
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:30:21.823814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median400
Q3600
95-th percentile1045
Maximum2500
Range2500
Interquartile range (IQR)360

Descriptive statistics

Standard deviation381.12807
Coefficient of variation (CV)0.8557999
Kurtosis8.4346225
Mean445.34718
Median Absolute Deviation (MAD)180
Skewness2.3187772
Sum86842.7
Variance145258.61
MonotonicityNot monotonic
2024-11-18T14:30:21.939946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
0.5%
600 14
 
0.3%
500 9
 
0.2%
450 8
 
0.2%
400 8
 
0.2%
300 8
 
0.2%
550 6
 
0.1%
350 4
 
0.1%
650 3
 
0.1%
275 3
 
0.1%
Other values (92) 110
 
2.7%
(Missing) 3868
95.2%
ValueCountFrequency (%)
0 22
0.5%
42 1
 
< 0.1%
43 1
 
< 0.1%
60 2
 
< 0.1%
75 2
 
< 0.1%
85 1
 
< 0.1%
90 1
 
< 0.1%
100 1
 
< 0.1%
102 1
 
< 0.1%
105 1
 
< 0.1%
ValueCountFrequency (%)
2500 1
 
< 0.1%
2200 1
 
< 0.1%
2000 1
 
< 0.1%
1830 1
 
< 0.1%
1813 1
 
< 0.1%
1700 1
 
< 0.1%
1200 3
0.1%
1150 1
 
< 0.1%
1000 1
 
< 0.1%
900 3
0.1%

when_was_solar_system_installed
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size192.5 KiB
1.0
306 
0.0
99 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 306
 
7.5%
0.0 99
 
2.4%
(Missing) 3658
90.0%

Length

2024-11-18T14:30:22.038947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:22.118400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 306
75.6%
0.0 99
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0 504
41.5%
. 405
33.3%
1 306
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 504
41.5%
. 405
33.3%
1 306
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 504
41.5%
. 405
33.3%
1 306
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 504
41.5%
. 405
33.3%
1 306
25.2%

have_system_to_store_backup_energy
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
0
3759 
1
 
304

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

Length

2024-11-18T14:30:22.201071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:22.556640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3759
92.5%
1 304
 
7.5%

method_of_receiving_water
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7740586
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:30:22.630817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median4
Q34
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0727576
Coefficient of variation (CV)0.28424509
Kurtosis12.844193
Mean3.7740586
Median Absolute Deviation (MAD)0
Skewness0.16627555
Sum15334
Variance1.1508089
MonotonicityNot monotonic
2024-11-18T14:30:22.717810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 3448
84.9%
1 375
 
9.2%
3 108
 
2.7%
6 75
 
1.8%
5 17
 
0.4%
7 14
 
0.3%
12 11
 
0.3%
2 9
 
0.2%
10 6
 
0.1%
ValueCountFrequency (%)
1 375
 
9.2%
2 9
 
0.2%
3 108
 
2.7%
4 3448
84.9%
5 17
 
0.4%
6 75
 
1.8%
7 14
 
0.3%
10 6
 
0.1%
12 11
 
0.3%
ValueCountFrequency (%)
12 11
 
0.3%
10 6
 
0.1%
7 14
 
0.3%
6 75
 
1.8%
5 17
 
0.4%
4 3448
84.9%
3 108
 
2.7%
2 9
 
0.2%
1 375
 
9.2%

water_heating_method_for_bathing
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5550086
Minimum0
Maximum7
Zeros507
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:30:22.799021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4921075
Coefficient of variation (CV)0.44862351
Kurtosis0.68439304
Mean5.5550086
Median Absolute Deviation (MAD)0
Skewness-1.5616836
Sum22570
Variance6.2105996
MonotonicityNot monotonic
2024-11-18T14:30:22.885362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 2457
60.5%
6 746
 
18.4%
0 507
 
12.5%
1 188
 
4.6%
5 99
 
2.4%
3 52
 
1.3%
4 14
 
0.3%
ValueCountFrequency (%)
0 507
 
12.5%
1 188
 
4.6%
3 52
 
1.3%
4 14
 
0.3%
5 99
 
2.4%
6 746
 
18.4%
7 2457
60.5%
ValueCountFrequency (%)
7 2457
60.5%
6 746
 
18.4%
5 99
 
2.4%
4 14
 
0.3%
3 52
 
1.3%
1 188
 
4.6%
0 507
 
12.5%

does_water_heating_equipment_serve_other_housing_units
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing3302
Missing (%)81.3%
Memory size192.5 KiB
0.0
694 
1.0
 
67

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2283
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 694
 
17.1%
1.0 67
 
1.6%
(Missing) 3302
81.3%

Length

2024-11-18T14:30:22.983620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:23.064680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 694
91.2%
1.0 67
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 1455
63.7%
. 761
33.3%
1 67
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1455
63.7%
. 761
33.3%
1 67
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1455
63.7%
. 761
33.3%
1 67
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1455
63.7%
. 761
33.3%
1 67
 
2.9%

household_members_used_hot_water_last_week
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing2457
Missing (%)60.5%
Memory size192.5 KiB
1.0
1014 
0.0
592 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1014
25.0%
0.0 592
 
14.6%
(Missing) 2457
60.5%

Length

2024-11-18T14:30:23.148930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:23.231415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1014
63.1%
0.0 592
36.9%

Most occurring characters

ValueCountFrequency (%)
0 2198
45.6%
. 1606
33.3%
1 1014
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2198
45.6%
. 1606
33.3%
1 1014
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2198
45.6%
. 1606
33.3%
1 1014
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2198
45.6%
. 1606
33.3%
1 1014
21.0%

boil_water_before_drinking
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
0
2233 
1
1830 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

Length

2024-11-18T14:30:23.314909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:23.394838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

Most occurring characters

ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2233
55.0%
1 1830
45.0%

source_of_energy_for_boiling_drinking_water
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)0.4%
Missing2233
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean1.7852459
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.5 KiB
2024-11-18T14:30:23.467752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2033763
Coefficient of variation (CV)0.67406756
Kurtosis3.3340123
Mean1.7852459
Median Absolute Deviation (MAD)0
Skewness1.680869
Sum3267
Variance1.4481146
MonotonicityNot monotonic
2024-11-18T14:30:23.556014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1102
27.1%
2 382
 
9.4%
4 305
 
7.5%
3 24
 
0.6%
5 10
 
0.2%
9 5
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 2233
55.0%
ValueCountFrequency (%)
1 1102
27.1%
2 382
 
9.4%
3 24
 
0.6%
4 305
 
7.5%
5 10
 
0.2%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
5 10
 
0.2%
4 305
 
7.5%
3 24
 
0.6%
2 382
 
9.4%
1 1102
27.1%

no_of_times_food_cooked_last_week
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
3
2040 
2
1386 
4
355 
1
230 
5
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

Length

2024-11-18T14:30:23.651283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:23.740646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

Most occurring characters

ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2040
50.2%
2 1386
34.1%
4 355
 
8.7%
1 230
 
5.7%
5 52
 
1.3%

gas_used_for_cooking
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
1
3564 
2
499 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%

Length

2024-11-18T14:30:23.834775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:23.915202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%

Most occurring characters

ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3564
87.7%
2 499
 
12.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
2494 
1
1569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%

Length

2024-11-18T14:30:23.997823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.078044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%

Most occurring characters

ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2494
61.4%
1 1569
38.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4016 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

Length

2024-11-18T14:30:24.161109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.239646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4016
98.8%
1 47
 
1.2%

firewood_used_for_cooking
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
3052 
1
1011 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

Length

2024-11-18T14:30:24.322200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.401495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

Most occurring characters

ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3052
75.1%
1 1011
 
24.9%

kerosene_used_for_cooking
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4004 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

Length

2024-11-18T14:30:24.486809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.565755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

Most occurring characters

ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4004
98.5%
1 59
 
1.5%

sawdust_or_paddy_husk_used_for_cooking
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4055 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Length

2024-11-18T14:30:24.647782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.725766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4055
99.8%
1 8
 
0.2%

biogas_used_for_cooking
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4063 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4063
100.0%

Length

2024-11-18T14:30:24.808149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:24.884467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4063
100.0%

Most occurring characters

ValueCountFrequency (%)
2 4063
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4063
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4063
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4063
100.0%

coconut_shells_or_charcoal_used_for_cooking
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4062 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

Length

2024-11-18T14:30:24.964043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:25.042617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4062
> 99.9%
1 1
 
< 0.1%

other_methods_used_for_cooking
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
2
4010 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Length

2024-11-18T14:30:25.123349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:30:25.203475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Most occurring characters

ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4010
98.7%
1 53
 
1.3%

Interactions

2024-11-18T14:30:17.948348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.105607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.492221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.865321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.532566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:18.031232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.182892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.570257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.940184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.610242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:18.103102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.258781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.642146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.280364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.687045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:18.189827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.333335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.714691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.357518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.769193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:18.281320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.412259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:16.791010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.444571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:30:17.858367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-18T14:30:25.291421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
aware_of_no_of_units_generated_by_solar_systemboil_water_before_drinkingcoconut_shells_or_charcoal_used_for_cookingdoes_water_heating_equipment_serve_other_housing_unitselectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookinggas_used_for_cookinggenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_other_methodsgenerate_electicity_using_solar_energygenerate_electicity_using_wind_powerhave_backup_generatorhave_system_to_store_backup_energyhousehold_members_used_hot_water_last_weekkerosene_used_for_cookingmethod_of_receiving_waterno_of_times_food_cooked_last_weekno_of_units_generated_by_solar_systemother_methods_used_for_cookingsawdust_or_paddy_husk_used_for_cookingsolar_energy_used_for_car_chargingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_water_heatingsolar_system_invertor_or_noninvertorsolar_system_ongrid_or_offgirdsource_of_energy_for_boiling_drinking_waterwater_heating_method_for_bathingwhen_was_solar_system_installed
aware_of_no_of_units_generated_by_solar_system1.0000.0001.0000.0000.0000.1410.0670.0690.0000.0000.0001.0000.0000.0000.0810.0940.0350.0000.1371.0000.0001.0000.0000.0000.0000.2670.2640.2840.1000.0710.000
boil_water_before_drinking0.0001.0000.0000.0510.0000.0350.0140.0170.0000.0000.0000.0350.0000.0000.0290.0970.0000.0240.1290.2080.0700.0000.0000.0000.0000.0000.0000.0001.0000.1360.050
coconut_shells_or_charcoal_used_for_cooking1.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0000.0001.000
does_water_heating_equipment_serve_other_housing_units0.0000.0511.0001.0000.0480.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0290.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0470.000
electricity_from_national_grid_used_for_cooking0.0000.0000.0000.0481.0000.0000.1690.1710.0000.0040.0000.1190.0180.0530.0520.0700.0160.0530.1230.0000.0880.0000.0000.0000.0000.1630.0510.0540.2000.1890.000
electricity_generated_using_solar_energy_used_for_cooking0.1410.0350.0000.0000.0001.0000.0410.0210.0000.0000.0000.2440.0000.0420.1130.0590.0000.0390.0000.0000.0000.0000.0000.0000.0000.3940.0960.0000.3990.2030.000
firewood_used_for_cooking0.0670.0140.0000.0020.1690.0411.0000.4440.0000.0000.0140.1750.0000.0700.1030.1070.0000.1710.0900.0000.0510.0421.0000.0000.0000.0000.0430.0000.6210.2430.040
gas_used_for_cooking0.0690.0170.0000.0000.1710.0210.4441.0000.0240.0000.0050.0660.0000.0300.0480.0700.1390.1050.1680.0000.2900.0401.0001.0001.0000.0000.0000.0000.5530.1270.000
generate_electicity_using_bio_energy0.0000.0000.0000.0000.0000.0000.0000.0241.0000.0310.0170.0000.0000.0000.0000.0190.0000.0000.0390.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0120.000
generate_electicity_using_mini_hydropower0.0000.0000.0000.0000.0040.0000.0000.0000.0311.0000.0000.0000.0510.0400.0110.0000.0000.0000.0001.0000.0000.0001.0001.0001.0001.0000.0000.0000.1890.0560.000
generate_electicity_using_other_methods0.0000.0000.0000.0000.0000.0000.0140.0050.0170.0001.0000.0000.0000.0220.0660.0000.0000.0000.0320.0000.0000.0001.0001.0001.0001.0000.0000.0000.1570.0780.000
generate_electicity_using_solar_energy1.0000.0350.0000.0000.1190.2440.1750.0660.0000.0000.0001.0000.0000.2510.2470.2100.0050.0320.0601.0000.0000.0001.0001.0001.0001.0001.0001.0000.3450.5731.000
generate_electicity_using_wind_power0.0000.0000.0000.0000.0180.0000.0000.0000.0000.0510.0000.0001.0000.0000.0000.0290.0000.0000.0281.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
have_backup_generator0.0000.0000.0000.0000.0530.0420.0700.0300.0000.0400.0220.2510.0001.0000.0900.0530.0000.0520.0570.0770.0000.0001.0000.0000.0000.0000.0700.0730.1270.1830.094
have_system_to_store_backup_energy0.0810.0290.0000.0000.0520.1130.1030.0480.0000.0110.0660.2470.0000.0901.0000.0840.0000.0080.0220.1240.0000.0000.0000.0000.0000.3850.1730.0000.1630.1980.000
household_members_used_hot_water_last_week0.0940.0971.0000.0440.0700.0590.1070.0700.0190.0000.0000.2100.0290.0530.0841.0000.0000.1030.0470.0000.0000.0001.0001.0000.2180.1850.0000.1490.0930.3330.000
kerosene_used_for_cooking0.0350.0000.0000.0000.0160.0000.0000.1390.0000.0000.0000.0050.0000.0000.0000.0001.0000.0250.0120.0000.0000.0001.0001.0001.0001.0000.0000.0000.4190.0330.013
method_of_receiving_water0.0000.0240.0000.0000.0530.0390.1710.1050.0000.0000.0000.0320.0000.0520.0080.1030.0251.0000.033-0.0060.0950.0001.0001.0001.0000.0410.0000.000-0.0730.0350.053
no_of_times_food_cooked_last_week0.1370.1290.0000.0290.1230.0000.0900.1680.0390.0000.0320.0600.0280.0570.0220.0470.0120.0331.0000.0410.4400.0001.0000.0000.0000.0000.0000.0840.0470.0570.031
no_of_units_generated_by_solar_system1.0000.2081.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0770.1240.0000.000-0.0060.0411.0000.2561.0001.0000.0001.0000.3250.0000.1500.2670.0170.000
other_methods_used_for_cooking0.0000.0700.0000.0000.0880.0000.0510.2900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.4400.2561.0000.0001.0001.0001.0000.0000.0750.0000.0000.0440.000
sawdust_or_paddy_husk_used_for_cooking1.0000.0000.0001.0000.0000.0000.0420.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.5770.0071.000
solar_energy_used_for_car_charging0.0000.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
solar_energy_used_for_cooking0.0000.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0000.9530.0001.0000.0000.5310.000
solar_energy_used_for_outdoor_lighting0.0000.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0000.0000.2181.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.7560.000
solar_energy_used_for_water_heating0.2670.0001.0000.0000.1630.3940.0000.0001.0001.0001.0001.0001.0000.0000.3850.1851.0000.0410.0000.3250.0001.0001.0000.9531.0001.0000.2911.0000.1180.1910.319
solar_system_invertor_or_noninvertor0.2640.0001.0000.0000.0510.0960.0430.0000.0000.0000.0001.0000.0000.0700.1730.0000.0000.0000.0000.0000.0751.0001.0000.0000.0000.2911.0000.2720.0000.1570.000
solar_system_ongrid_or_offgird0.2840.0001.0000.0000.0540.0000.0000.0000.0000.0000.0001.0000.0000.0730.0000.1490.0000.0000.0840.1500.0001.0001.0001.0001.0001.0000.2721.0000.2310.1910.077
source_of_energy_for_boiling_drinking_water0.1001.0001.0000.0000.2000.3990.6210.5530.0000.1890.1570.3450.0000.1270.1630.0930.419-0.0730.0470.2670.0000.5771.0000.0000.0000.1180.0000.2311.000-0.0380.160
water_heating_method_for_bathing0.0710.1360.0000.0470.1890.2030.2430.1270.0120.0560.0780.5730.0000.1830.1980.3330.0330.0350.0570.0170.0440.0071.0000.5310.7560.1910.1570.191-0.0381.0000.235
when_was_solar_system_installed0.0000.0501.0000.0000.0000.0000.0400.0000.0000.0000.0001.0000.0000.0940.0000.0000.0130.0530.0310.0000.0001.0000.0000.0000.0000.3190.0000.0770.1600.2351.000

Missing values

2024-11-18T14:30:18.448989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T14:30:18.847667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-18T14:30:19.153229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

have_backup_generatorgenerate_electicity_using_solar_energygenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_wind_powergenerate_electicity_using_other_methodssolar_system_ongrid_or_offgirdsolar_system_invertor_or_noninvertorsolar_energy_used_for_water_heatingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_car_chargingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_abovesolar_energy_used_for_other_purposesaware_of_no_of_units_generated_by_solar_systemno_of_units_generated_by_solar_systemwhen_was_solar_system_installedhave_system_to_store_backup_energymethod_of_receiving_waterwater_heating_method_for_bathingdoes_water_heating_equipment_serve_other_housing_unitshousehold_members_used_hot_water_last_weekboil_water_before_drinkingsource_of_energy_for_boiling_drinking_waterno_of_times_food_cooked_last_weekgas_used_for_cookingelectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookingkerosene_used_for_cookingsawdust_or_paddy_husk_used_for_cookingbiogas_used_for_cookingcoconut_shells_or_charcoal_used_for_cookingother_methods_used_for_cooking
household_ID
ID0001222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN0.014.02112122222
ID0002222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN0NaN4122222222
ID00032122221.01.0NaNNaNNaNNaNNaNNaNNone1.01200.01.01400.01.012.03212222222
ID0004222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN0.011.03122222222
ID0005222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN067NaNNaN12.02122222222
ID0006222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN0NaN5122222222
ID0007222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN12.03122222222
ID0008222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN045NaN0.012.05112222222
ID0009222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN0.014.03222122222
ID0010222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN14.03212122222
have_backup_generatorgenerate_electicity_using_solar_energygenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_wind_powergenerate_electicity_using_other_methodssolar_system_ongrid_or_offgirdsolar_system_invertor_or_noninvertorsolar_energy_used_for_water_heatingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_car_chargingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_abovesolar_energy_used_for_other_purposesaware_of_no_of_units_generated_by_solar_systemno_of_units_generated_by_solar_systemwhen_was_solar_system_installedhave_system_to_store_backup_energymethod_of_receiving_waterwater_heating_method_for_bathingdoes_water_heating_equipment_serve_other_housing_unitshousehold_members_used_hot_water_last_weekboil_water_before_drinkingsource_of_energy_for_boiling_drinking_waterno_of_times_food_cooked_last_weekgas_used_for_cookingelectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookingkerosene_used_for_cookingsawdust_or_paddy_husk_used_for_cookingbiogas_used_for_cookingcoconut_shells_or_charcoal_used_for_cookingother_methods_used_for_cooking
household_ID
ID4054222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN11.03112222222
ID40552122221.01.0NaNNaNNaNNaNNaNNaNNone1.0100.01.00410.01.011.02111222222
ID4056222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN0.00NaN3122122222
ID4057222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN0NaN3222122222
ID4058222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN146NaN0.014.03122122222
ID4059222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN1.011.04112222222
ID4060222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN0127NaNNaN14.02222122222
ID4061222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN0NaN2222212222
ID4062222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN046NaN0.011.02122222222
ID4063222222NaNNaNNaNNaNNaNNaNNaNNaNNoneNaNNaNNaN047NaNNaN11.02122222222

Duplicate rows

Most frequently occurring

have_backup_generatorgenerate_electicity_using_solar_energygenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_wind_powergenerate_electicity_using_other_methodssolar_system_ongrid_or_offgirdsolar_system_invertor_or_noninvertorsolar_energy_used_for_water_heatingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_car_chargingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_aboveaware_of_no_of_units_generated_by_solar_systemno_of_units_generated_by_solar_systemwhen_was_solar_system_installedhave_system_to_store_backup_energymethod_of_receiving_waterwater_heating_method_for_bathingdoes_water_heating_equipment_serve_other_housing_unitshousehold_members_used_hot_water_last_weekboil_water_before_drinkingsource_of_energy_for_boiling_drinking_waterno_of_times_food_cooked_last_weekgas_used_for_cookingelectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookingkerosene_used_for_cookingsawdust_or_paddy_husk_used_for_cookingbiogas_used_for_cookingcoconut_shells_or_charcoal_used_for_cookingother_methods_used_for_cooking# duplicates
235222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN3122222222221
226222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN2122222222216
257222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN11.03122222222150
233222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN3112222222149
223222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN2112222222111
253222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN11.0212222222283
255222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN11.0311222222272
234222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN312212222264
251222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN11.0211222222257
218222222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN047NaNNaN0NaN112222222256